As the mainstream architecture for 6G networks, the Space-Air-Ground Integrated Network (SAGIN) faces dual challenges of security threats introduced by Network Function Virtualization and inherent topology dynamics, necessitating a secure and flexible Service Function Chain (SFC) deployment paradigm. This paper proposes a novel SFC security framework integrating Deception Defense (DD) and Moving Target Defense (MTD). A security assessment model is introduced to quantify virtualization risks, complemented by a DD-driven deployment strategy that incorporates security awareness and load balancing to enhance attack obfuscation. Furthermore, to mitigate the impact of topology dynamics, a link-stability-aware migration algorithm is designed with MTD, effectively balancing security and service continuity. Extensive simulations demonstrate that compared to conventional methods, the framework improves SFC security by approximately 102%, optimizes network load balancing by 23%, and reduces migration frequency by 31%, thereby ensuring service stability in highly dynamic scenarios. This work effectively bridges the critical gap between proactive security enforcement and adaptive resource orchestration in SAGIN environments.

With the development and popularization of virtualization technology, it offers efficient, flexible services in cloud computing, data centers, etc. However, the sharing of underlying physical devices makes the isolation between different virtual machines (VMs) relatively fragile, leading to new security challenges. Co-resident attack is one of the most representative types. In order to facilitate the construction of side channels to achieve sensitive data theft and other malicious operations, attackers try to co-locate their VMs with target VMs on the same physical device. This paper proposes a VM placement method for virtualization cloud platforms to mitigate the risks posed by co-resident attacks. This study model the VM placement process in the virtualization cloud platform as a Markov Decision Process (MDP) and construct an objective function to minimize co-resident attacks under various constraints. Furthermore, it is believe that after the co-residence occurs, it is equally important to prevent attackers from constructing side channels against the co-resident targets. Therefore, the concept of deceptive defense is introduced to mislead attackers. In addition, this study introduces reinforcement learning and designs a deceptive VM deployment strategy generation method based on PPO (DD-PPO), considering platform security, load balance, and power consumption. Finally, CloudSim is used to build the simulation environment, and the performance of the proposed algorithm is evaluated through experiments. Simulation results show that the proposed algorithm effectively mitigates the threats of co-resident attacks compared to baselines.

Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.

Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. But in real distributed scenarios, Non-Independent and Identically Distributed(Non-IID) of data reduces the speed of learning and the accuracy of global model. To solve this problem, this paper proposes a federated learning client selection algorithm based on cluster label information(FedCLS). FedCLS realizes efficient federated learning by optimizing the selection of clients in each round of training. Through extensive simulations, we demonstrate that compared with traditional FedAVG based on random extraction, FedCLS has better learning performance and less resource overhead.

Based only on practical phasor measurements from the CampusWAMS, the paper presents the method of modeling Kita-Hon HVDC Link for load frequency control analysis of Eastern Japan 50-Hz power system. Kita-Hon HVDC Link is equipped with automatic frequency control and serves as the only interconnection between Hokkaido grid and Honshu grid of Eastern Japan 50-Hz power system. Firstly, the effect and its characteristic of Kita-Hon HVDC Link on load frequency control are observed based on practical phasor measurements. A simulation model describing the observed effect of Kita-Hon HVDC Link is then constructed. The unknown parameters of the simulation model are optimally estimated using real phasor measurements. The evaluation results obtained by simulating the constructed model based on practical phasor measurements are given to show the effectiveness of the proposed method.